# -*- coding: utf-8 -*-
"""
Created on Mon Apr  3 12:05:21 2017
@author: YoungHao
"""


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
import zipfile
import jieba
import pickle
import codecs
import numpy as np
import urllib
#from six.moves import range  # pylint: disable=redefined-builtin
import tensorflow as tf
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--dataPath")
arguments = parser.parse_args()
dataPath = arguments.dataPath
print("dataPath: ",dataPath)  

stop_symbol = '()，．。！,ゅ\'～~`·？-=:、‘；[]{}*&^%$#@!~+_=-！@#￥%……&*（）——+}{【】||？。><！，;.1234567890：“”"》《+?/%）（@ \n \t \u3000'
out_dir = os.path.abspath(os.path.join(os.path.curdir, 'semanticsim',"stopwords.txt"))
f_stop = open(out_dir,encoding= 'utf-8')  
try:  
    f_stop_text = f_stop.read( )
finally:  
    f_stop.close( ) 
f_stop_seg_list=f_stop_text.split('\n')

final_dictionary = dict()
def read_sentence(filename):
    sentence = [[]]
    for line in open(filename, encoding = 'utf-8'):
        da = []
        seg_list = jieba.cut(line.strip(), cut_all=False)
        buffer = "/".join(seg_list)
        da = buffer.split("/")
        print(da)
        sen_buf = []
        for i in da:
            if i in final_dictionary:
                sen_buf.append(final_dictionary[i])
            else:
                sen_buf.append(final_dictionary['UNK'])
        sentence.append(sen_buf)
    a = 0
    max_len = len(sentence) - 1
    for i in sentence:
        if a < max_len:
            sentence[a] = sentence[a + 1]
            a = a + 1
        else:
            break
    del sentence[max_len - 1]
    for sen in sentence:              #128 * 128
        while len(sen) < 128:
            sen.append(np.zeros(128, dtype = np.float32))
    return sentence
        
    

def read_data(filename):
    """Extract the first file enclosed in a zip file as a list of words"""
    book = ''
    for line in open(filename, encoding = 'utf-8'):
        book = book + line
    #da = []
    data = []
    seg_list = jieba.cut(book, cut_all=False)
    buffer = "/".join(seg_list)
    da = buffer.split("/")
    for symbol in da:
        if symbol not in f_stop_seg_list and symbol not in stop_symbol:
            data.append(symbol)

    return data
  
def read_labels(filename):
    """Extract the first file enclosed in a zip file as a list of words"""
    labels = []
    for line in open(filename, encoding = 'utf-8'):
        labels.append(line.strip())
    return labels
words = read_data(dataPath)   
#sentence = read_sentence('sentence.txt')
#sen_labels = read_labels('labels.txt')
print('Data size', len(words))

# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 100

def build_dataset(words):
    count = [['UNK', -1]]
    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary)
    print("dict: ",dictionary)
    data = list()
    unk_count = 0
    for word in words:
        if word in dictionary:
            index = dictionary[word]
        else:
            index = 0  # dictionary['UNK']
            unk_count += 1
        data.append(index)
    print("data: ",data)
    count[0][1] = unk_count
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    print("reverse_dictionary: ",reverse_dictionary)
    return data, count, dictionary, reverse_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(words)
del words  # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0


# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
    buffer = collections.deque(maxlen=span)
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    for i in range(batch_size // num_skips):
        target = skip_window  # target label at the center of the buffer
        targets_to_avoid = [skip_window]
        for j in range(num_skips):
            while target in targets_to_avoid:
                target = random.randint(0, span - 1)
            targets_to_avoid.append(target)
            batch[i * num_skips + j] = buffer[skip_window]
            labels[i * num_skips + j, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    return batch, labels

batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]],
        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 64       #batch_size 越小越好
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 1       # How many words to consider left and right.
num_skips = 2         # How many times to reuse an input to generate a label.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
num_sampled = 32    # Number of negative examples to sample.

graph = tf.Graph()

with graph.as_default():
# Input data.
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Ops and variables pinned to the CPU because of missing GPU implementation
    with tf.device('/cpu:0'):
    # Look up embeddings for inputs.
        embeddings = tf.Variable(
            tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the NCE loss
        nce_weights = tf.Variable(
            tf.truncated_normal([vocabulary_size, embedding_size],
                            stddev=1.0 / math.sqrt(embedding_size)))
        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    loss = tf.reduce_mean(
        tf.nn.nce_loss(weights=nce_weights,
                     biases=nce_biases,
                     labels=train_labels,
                     inputs=embed,
                     num_sampled=num_sampled,
                     num_classes=vocabulary_size))

    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

    # Compute the cosine similarity between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)

    # Add variable initializer.
    init = tf.global_variables_initializer()

# Step 5: Begin training.
num_steps = 1000   #01

with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    init.run()
    print("Initialized")

    average_loss = 0
    for step in range(num_steps):
        batch_inputs, batch_labels = generate_batch(
            batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

    # We perform one update step by evaluating the optimizer op (including it
    # in the list of returned values for session.run()
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
        # The average loss is an estimate of the loss over the last 2000 batches.
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0

        # Note that this is expensive (~20% slowdown if computed every 500 steps)
        if step % 10000 == 0:
            sim = similarity.eval()
            for i in range(valid_size):
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # number of nearest neighbors
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = "Nearest to %s:" % valid_word
                for k in range(top_k):
                    close_word = reverse_dictionary[nearest[k]]
                    log_str = "%s %s," % (log_str, close_word)
    final_embeddings = normalized_embeddings.eval()

# Step 6: Visualize the embeddings.


def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
    plt.figure(figsize=(18, 18))  # in inches
    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.scatter(x, y)
        plt.annotate(label,
                 xy=(x, y),
                 xytext=(5, 2),
                 textcoords='offset points',
                 ha='right',
                 va='bottom')

    plt.savefig(filename)

try:
    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt

    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    plot_only = 500
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
    labels = [reverse_dictionary[i] for i in range(plot_only)]
    plot_with_labels(low_dim_embs, labels)

except ImportError:
    print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
  
final_dictionary = dict()

def final2dictionary():
    for i in range(100):
        final_dictionary[reverse_dictionary[i]] = final_embeddings[i]

final2dictionary()
    
def dictionary2pickle():
    lolfile = os.path.join(os.path.curdir, 'semanticsim',"lol.txt")
    with codecs.open(lolfile,"wb+","utf-8") as f:  
        f.write(str(vocabulary_size)+' 50\n')    
        for key, value in final_dictionary.items(): 
            cont=str(list(value)).replace(',','').replace('[','').replace(']', '')
            f.write(key+' '+cont+'\n')
dictionary2pickle()